Which of the following is an example of unsupervised learning technique?

Question

BlackTom AI · Accepted Answer

Co-occurrence grouping is an example of unsupervised learning because it discovers structure in unlabeled data.

1. Relevant knowledge points and concepts
- Supervised learning: algorithms learn a mapping from inputs to known labels (examples: classification trees, regression). Training data include target labels.
- Unsupervised learning: algorithms discover patterns, clusters, or associations in data without labeled outputs (examples: clustering, association/co-occurrence mining, dimensionality reduction).
- Co-occurrence grouping refers to methods that find which items/events tend to appear together (association rules, market-basket analysis, co-occurrence matrices), which is inherently exploratory and label-free.

2. Step-by-step reasoning
- Examine each option and classify it as supervised or unsupervised:
  a) Co-occurrence grouping — finds associations among items without target labels → unsupervised.
  b) Classification tree — constructs a model that predicts discrete class labels from features → supervised.
  c) Causal modeling — aims to infer cause–effect relationships; typically requires labeled outcomes or interventions and is not a standard unsupervised discovery method (it’s model-driven and often treated separately from basic unsupervised techniques).
  d) Regression analysis — estimates continuous target values from predictors → supervised.

3. Intermediate steps (mapping options to categories)
- Option a ⇒ unsupervised: no labels needed, looks for frequent itemsets/co-occurrence patterns.
- Option b ⇒ supervised: needs labeled classes to train decision tree.
- Option c ⇒ not a canonical unsupervised technique: requires specification of causal variables/outcomes; often uses experimental/observational labels.
- Option d ⇒ supervised: fits a function to labeled numerical targets.

4. Why the correct answer is correct
- Co-occurrence grouping uncovers relationships and groupings purely from input data frequencies and co-occurrence statistics. Because it does not rely on pre-assigned target labels, it matches the definition of unsupervised learning (e.g., association rule mining, clustering).

5. Why the other options are incorrect
- Classification tree: requires labeled classes and is a supervised classification technique.
- Causal modeling: focuses on causal inference rather than pure unlabeled pattern discovery and typically involves modeling directed relationships with outcome variables.
- Regression analysis: requires numeric target variables to learn predictions and is a supervised method.

6. Conclusion
The correct answer is Co-occurrence grouping. This matches the provided answer: "Co-occurrence grouping."

Which of the following is an example of unsupervised learning technique?Single choice

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